Erfan Soltanmohammadi

A Survey of Traffic Issues in Machine-to-Machine Communications Over LTE

Machine-to-machine (M2M) communication, also referred to as Internet of Things (IoT), is a global network of devices such as sensors, actuators, and smart appliances which collect information, and can be controlled and managed in real time over the Internet. Due to their universal coverage, cellular networks and the Internet together offer the most promising foundation for the implementation of M2M communication. With the worldwide deployment of the fourth generation (4G) of cellular networks, the long-term evolution (LTE) and LTE-advanced standards have defined several quality-of-service classes to accommodate the M2M traffic. However, cellular networks are mainly optimized for human-to-human (H2H) communication. The characteristics of M2M traffic are different from the human-generated traffic and consequently create sever problems in both radio access and the core networks (CNs). This survey on M2M communication in LTE/LTE-A explores the issues, solutions, and the remaining challenges to enable and improve M2M communication over cellular networks. We first present an overview of the LTE networks and discuss the issues related to M2M applications on LTE. We investigate the traffic issues of M2M communications and the challenges they impose on both access channel and traffic channel of a radio access network and the congestion problems they create in the CN. We present a comprehensive review of the solutions for these problems which have been proposed in the literature in recent years and discuss the advantages and disadvantages of each method. The remaining challenges are also discussed in detail.

Blind Modulation Classification over Fading Channels Using Expectation-Maximization

We propose a blind modulation classification algorithm when the channel coefficient, the noise power and the energy of the transmitted signal are unknown at the receiver. First, under each candidate modulation scheme, we evaluate the unknown parameters using the iterative expectation maximization algorithm. Modulation classification is then accomplished by minimizing the distance between the log-likelihood of the received data and the expected log-likelihood under each candidate modulation scheme. Results are presented from simulations in terms of detection probability vs. SNR for the class of BPSK, QPSK, 16QAM and 64QAM modulation schemes. The results show a significant improvement over QHLRT and are very close to the upper bound ALRT-UB [1].

Fast Detection of Malicious Behavior in Cooperative Spectrum Sensing

In this paper we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving nodes. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. In contrast to previous work we assume that the FC has no prior information about the radios in the network except that the honest radios are in majority. As shown in the paper this is required for any algorithm to uniquely identify the CRs. Another distinguishing feature is that our approach can classify the radios into more than just two classes of honest and malicious CRs. This applies in cases where the honest CRs have different detection and false alarm probabilities, which may arise when they employ different spectrum sensing techniques or encounter dissimilar channel and noise conditions. Another case is when the CRN includes more than one type of misbehaving CRs. Our numerical results show significant improvements over the widely popular reputation-based classifier (RBC). In particular, with only a few decisions from the CRs, the proposed algorithm can quickly and efficiently classify the CRs whereas the RBC method fails even for networks with a large number of CRs. In all of our numerical results the EM algorithm converged in five or fewer iterations resulting in fast convergence of the proposed method. This makes the proposed method a good candidate for implementation in CRNs. The numerical results are also compared with the Cramer-Rao lower bound and show a close match. Simulation results are also presented to demonstrate the efficacy of the proposed algorithm in the presence of correlated observations among the radios.

Spectrum Sensing Over MIMO Channels Using Generalized Likelihood Ratio Tests

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. Many approaches have been proposed when both primary user and secondary user employ a single antenna. Recently several techniques have also been proposed assuming that the the secondary user employs multiple antennas. In this paper, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary users signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. Through analysis we exhibit the role of the eigenvalues in characterizing the signal+noise and noise subspaces in the received data. Simulation results are presented in terms of the receiver operating characteristics and detection probabilities for several cases of interest.

Improving the Sensing–Throughput Tradeoff for Cognitive Radios in Rayleigh Fading Channels

In-band spectrum sensing (SS) in overlay cognitive radio networks requires that the secondary users (SUs) periodically suspend their communication to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring (SM), the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). Previously, it has been shown that in additive white Gaussian noise channels, a hybrid SS/SM system significantly improves the channel utilization of SUs and the detection delay of PUs. In this paper, we investigate the problem of SM in the presence of fading, where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide good performance. Next, we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). However, for equal gain combining and selection combining, the inclusion of combiner coefficients does not improve the performance over REC alone. In the case of MRC, we evaluate the receiver operating characteristics (ROCs) from analysis and compare the results with those from simulations using a BCH code as well as a convolutional code. The results show a close match between analysis and simulation results. Channel utilization and detection delay are evaluated from simulations which show that with MRC and the proposed decision statistic, the hybrid SS/SM system significantly outperforms the SS alone.

Improving Detection Delay in Cognitive Radios Using Secondary-User Receiver Statistics

In-band spectrum sensing (SS) requires that secondary users (SUs) periodically interrupt their communication to detect the emergence of the primary users (PUs) in the channel. A new approach referred to as spectrum monitoring (SM) was proposed by Boyd et al., which allows the SU to employ its receiver statistics to detect the emergence of the PU during its own communication periods. In this paper, we construct a decision statistic for SM based on the SUs receiver error count. We then evaluate the detection probability of SM in the presence of interference from the PU signal. Next, we derive closed-form formulas for channel utilization and detection delay using two Markov chain models. Upper and lower bounds on channel utilization and detection delay are derived, and an optimization problem is formulated and solved to maximize channel utilization with a constraint on detection delay. Numerical results from analysis are compared with simulation results obtained for a BCH code and a convolutional code, which show the accuracy of the analysis and the significant improvement of a hybrid SM/SS over SS alone.

Performance analysis of spectrum monitoring for cognitive radios

In-band spectrum sensing protocols require that the secondary users (SUs) periodically suspend their transmission periods and sense the channel in order to determine whether the primary user (PU) has emerged or not. There is a tradeoff between the spectrum sensing period and the throughput of the SU, often referred to as sensing-throughput tradeoff. In this paper, using the receiver error count, we introduce a decision statistic to enable the SU to detect the emergence of the PU without having to interrupt its own communication. We derive closed form formulas for channel utilization of the SU and detection delay of the PU using two Markov chain models. The limits of performance for a system using the proposed decision statistic is derived and an optimization problem is solved to maximize channel utilization with a constraint on detection delay. Numerical results are presented from analysis and simulation which show the accuracy of the analysis and the proficiency of the proposed algorithm.

Tenor: A Measure of Central Tendency for Distributed Networks

We introduce a new tendency measure for a probability mass function (pmf) referred to as “tenor,” and defined in terms of the phase of the first non-zero frequency of the discrete Fourier transform of the pmf. This statistic is in the vicinity of the region of highest probability of the pmf. Unlike mean, tenor is robust against outliers, and unlike mode and median, tenor can be evaluated using only arithmetic operations of addition and multiplication, without the need for comparison operations. We propose a distributed algorithm for computation of tenor in a graph and prove that for large networks represented by Erdos-Renyi graphs, [1] and by Watts-Strogatz graphs (small-world graphs), [2] the distributed algorithm converges. Numerical examples including the distributed computation of the majority vote are presented to demonstrate the operation of the algorithm.

Semi-Blind Data Detection for Unitary Space-Time Modulation in MIMO Communications Systems

We propose an iterative algorithm based on expectation maximization (EM) to jointly estimate the system parameters and decode the data in a MIMO system using unitary space-time block codes. It is assumed that the receiver is unaware of the channel coefficients and their distribution, the average energy of the received signal, the prior probability of each signal in the constellation, and the noise power. The algorithm works for arbitrary modulation schemes and any channel model including Rayleigh and Rician fading. The complexity of the proposed receiver is computed and it is shown to be significantly lower than previously published methods, as it does not require any matrix inversion or trellis search. The performance of the proposed receiver is evaluated by simulations in terms of symbol error rate (SER) vs. signal-to-noise ratio (SNR) and it is shown that with only a few iterations of the algorithm it achieves a performance close to that of the receiver which knows all the parameters.

Spectrum monitoring for cognitive radios in Rayleigh fading channel

In-band spectrum sensing requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel or not. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). Previously it is shown that in AWGN channels, a hybrid spectrum sensing/spectrum monitoring system significantly improves channel utilization of the SUs and detection delay for the PUs. In this paper we investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. Simulation results are presented that show significant improvement in system performance.